<p>As artificial intelligence becomes increasingly embedded in academic life, it is beginning to reshape the cognitive, ethical, and institutional foundations of scholarly practice. This paper argues that large language models and automated research tools do more than enhance productivity, fundamentally altering how ideas are generated, valued, and legitimized. We conceptualize this transformation as a shift from the “natural selection of skills” to a “natural selection of minds,” where academic success increasingly depends on the ability to frame generative questions, synthesize across disciplines, and ethically navigate human–machine collaboration. Through interdisciplinary case studies and critical analysis, we highlight both the promises and the risks of this epistemic transition, emphasizing the need for reflection and potential reevaluation of academic standards to align with the realities of hybrid intelligence and cognitive inequality in the AI era. While our analysis captures dynamics most visible in fields shaped by publish-or-perish pressures and formalist conventions, it may not generalize equally across all disciplines.</p>